Figure 1

Quantitative metabolic phenotyping for continuous pathway modeling versus spectral-based black-and-white diagnostic classification. Right: A common metabolomics approach is to use the spectral data directly in a chemometrics model that explores the overall differences between individuals expected to belong to different diagnostic groups. This approach is not optimal for epidemiological or genetic research in which metabolic and disease continuum should be appreciated, because common disorders, being multigenic, are fundamentally quantitative traits. Therefore, the real data (strongly overlapping metabolic characteristics) do not match with the pre-defined groups (health versus disease). Left: New high-throughput methodologies involve sophisticated automation, including absolute quantification of identified metabolites. This provides new opportunities to understand disease etiologies and to handle disease risks and diagnostics as truly continuous multivariate phenomena. When these kinds of approaches mature and extensive datasets accumulate, it is anticipated that characteristic metabolic phenotypes for various disease-related pathways can be identified. This would allow overall assessment of individual health status and disease risks. Here, from the spectral NMR data of an individual, metabolites are identified and quantified in a fully automated manner, resulting in a comprehensive metabolic phenotype. Different pathways, which predispose to metabolic disorders in a distinctive way, have characteristic (time-dependent) metabolic signatures with specific risk distributions. In real life, metabolic disorders are interrelated and rarely exist in isolation.